1 citations · 4 across the 17 of their papers we have counts for
9 papers · 1 filter
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Bac Nguyen, Yuhta Takida, Naoki Murata +4
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between obj…
Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Yonghyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +7
While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identi…
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image
Anubhav Jain, Yuya Kobayashi, Naoki Murata +6
Watermarking techniques are vital for protecting intellectual property and preventing fraudulent use of media. Most previous watermarking schemes designed for diffusion models embe…
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
Michail Dontas, Yutong He, Naoki Murata +3
This paper considers blind inverse image restoration, the task of predicting a target image from a degraded source when the degradation (i.e. the forward operator) is unknown. Exis…
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida +4
Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discret…
MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training
Kengo Uchida, Takashi Shibuya, Yuhta Takida +4
In text-to-motion generation, controllability as well as generation quality and speed has become increasingly critical. The controllability challenges include generating a motion o…